{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TK56PYQRZF3L7V6Y727C63FY2J","short_pith_number":"pith:TK56PYQR","canonical_record":{"source":{"id":"1805.08297","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-21T21:36:09Z","cross_cats_sorted":[],"title_canon_sha256":"b2536f60d26c0d328d485b7ef660edcdf7f284a7a5ff7f4c6b94f4eeedaf89b9","abstract_canon_sha256":"d725560f93ae4b0018dba79def88a3010ff7502d15b8e7152d6d7493b5a55ee9"},"schema_version":"1.0"},"canonical_sha256":"9abbe7e211c976bfd7d8febe2f6cb8d246033e474fb891a87732627d3d999151","source":{"kind":"arxiv","id":"1805.08297","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08297","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08297v1","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08297","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"TK56PYQRZF3L","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TK56PYQRZF3L7V6Y","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TK56PYQR","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TK56PYQRZF3L7V6Y727C63FY2J","target":"record","payload":{"canonical_record":{"source":{"id":"1805.08297","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-21T21:36:09Z","cross_cats_sorted":[],"title_canon_sha256":"b2536f60d26c0d328d485b7ef660edcdf7f284a7a5ff7f4c6b94f4eeedaf89b9","abstract_canon_sha256":"d725560f93ae4b0018dba79def88a3010ff7502d15b8e7152d6d7493b5a55ee9"},"schema_version":"1.0"},"canonical_sha256":"9abbe7e211c976bfd7d8febe2f6cb8d246033e474fb891a87732627d3d999151","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:26.994037Z","signature_b64":"5EiuFFsRiUltvQLe8wXp+UTE1enLrFVXwFNo0En7MkjYuAyrJUkCWZLA7xXbmxibAPAv1QRcfNGvOYoLG/nYAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9abbe7e211c976bfd7d8febe2f6cb8d246033e474fb891a87732627d3d999151","last_reissued_at":"2026-05-18T00:15:26.993255Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:26.993255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.08297","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1borUBloLQPmgrXKScpHUCGL4VAG27zhaOrnHSbXu9ovpoKtl66+INaibg6oiBkPNzG9X6BJXxvtDU2CTQjJCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:40:40.960802Z"},"content_sha256":"5e1af3a63aae22a6dc7c227e2b66ffe6486085c1b629b71d819f7623850ce358","schema_version":"1.0","event_id":"sha256:5e1af3a63aae22a6dc7c227e2b66ffe6486085c1b629b71d819f7623850ce358"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TK56PYQRZF3L7V6Y727C63FY2J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Character-based Neural Networks for Sentence Pair Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Wei Xu, Wuwei Lan","submitted_at":"2018-05-21T21:36:09Z","abstract_excerpt":"Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural models for these tasks rely on pretrained word embedding and compose sentence-level semantics in varied ways; however, few works have attempted to verify whether we really need pretrained embeddings in these tasks. In this paper, we study how effective subword-level (character and character n-gram) representations are in sentence pair modeling. Though it is well-known that subword models are effective in tasks with s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08297","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xymj/ahTwBG7JnycE8d95/Eiu0OW8/X4lTlDvs/sMsbzc5j1Lf5irmHiLUz+4HwIHthd7+Xwb1a1RFbbSvZJCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:40:40.961154Z"},"content_sha256":"a26c0dee22d0a8b0c97012d8d38eb3291536f210c3f259e50f885aa837ab827d","schema_version":"1.0","event_id":"sha256:a26c0dee22d0a8b0c97012d8d38eb3291536f210c3f259e50f885aa837ab827d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TK56PYQRZF3L7V6Y727C63FY2J/bundle.json","state_url":"https://pith.science/pith/TK56PYQRZF3L7V6Y727C63FY2J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TK56PYQRZF3L7V6Y727C63FY2J/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T15:40:40Z","links":{"resolver":"https://pith.science/pith/TK56PYQRZF3L7V6Y727C63FY2J","bundle":"https://pith.science/pith/TK56PYQRZF3L7V6Y727C63FY2J/bundle.json","state":"https://pith.science/pith/TK56PYQRZF3L7V6Y727C63FY2J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TK56PYQRZF3L7V6Y727C63FY2J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TK56PYQRZF3L7V6Y727C63FY2J","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d725560f93ae4b0018dba79def88a3010ff7502d15b8e7152d6d7493b5a55ee9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-21T21:36:09Z","title_canon_sha256":"b2536f60d26c0d328d485b7ef660edcdf7f284a7a5ff7f4c6b94f4eeedaf89b9"},"schema_version":"1.0","source":{"id":"1805.08297","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08297","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08297v1","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08297","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"TK56PYQRZF3L","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TK56PYQRZF3L7V6Y","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TK56PYQR","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:a26c0dee22d0a8b0c97012d8d38eb3291536f210c3f259e50f885aa837ab827d","target":"graph","created_at":"2026-05-18T00:15:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural models for these tasks rely on pretrained word embedding and compose sentence-level semantics in varied ways; however, few works have attempted to verify whether we really need pretrained embeddings in these tasks. In this paper, we study how effective subword-level (character and character n-gram) representations are in sentence pair modeling. Though it is well-known that subword models are effective in tasks with s","authors_text":"Wei Xu, Wuwei Lan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-21T21:36:09Z","title":"Character-based Neural Networks for Sentence Pair Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08297","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5e1af3a63aae22a6dc7c227e2b66ffe6486085c1b629b71d819f7623850ce358","target":"record","created_at":"2026-05-18T00:15:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d725560f93ae4b0018dba79def88a3010ff7502d15b8e7152d6d7493b5a55ee9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-21T21:36:09Z","title_canon_sha256":"b2536f60d26c0d328d485b7ef660edcdf7f284a7a5ff7f4c6b94f4eeedaf89b9"},"schema_version":"1.0","source":{"id":"1805.08297","kind":"arxiv","version":1}},"canonical_sha256":"9abbe7e211c976bfd7d8febe2f6cb8d246033e474fb891a87732627d3d999151","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9abbe7e211c976bfd7d8febe2f6cb8d246033e474fb891a87732627d3d999151","first_computed_at":"2026-05-18T00:15:26.993255Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:26.993255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5EiuFFsRiUltvQLe8wXp+UTE1enLrFVXwFNo0En7MkjYuAyrJUkCWZLA7xXbmxibAPAv1QRcfNGvOYoLG/nYAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:26.994037Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.08297","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e1af3a63aae22a6dc7c227e2b66ffe6486085c1b629b71d819f7623850ce358","sha256:a26c0dee22d0a8b0c97012d8d38eb3291536f210c3f259e50f885aa837ab827d"],"state_sha256":"3e99f73a4c8eb94bbde241624f13bc90c15f8f4d7986544bb03115e1e03985f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+2A7vX0uXTvEuZH6/CU5lyT9IKw2gXXbTv8vMFdN24FxR1IlX0y++d2f9p42g40nd2qwbJqOby4NRQ1OZfHJBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T15:40:40.963112Z","bundle_sha256":"e25901f7ee38859fe709e91939df0d20e27e0365375f70914091b9d1b3ef9a3f"}}